基于退化引导的水下图像复原与面向任务的潜在控制
Degradation-Guided Underwater Image Restoration with Task-Oriented Latent Control
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中文总结 AI 辅助
针对水下图像退化信息双重作用被忽略的问题，提出PROTEUS模型，结合退化引导特征适应与面向任务的潜在控制，在多基准上实现了复原质量与计算成本的良好平衡。
中文摘要 AI 辅助
水下图像中的退化信息具有双重作用：其空间和光谱线索可引导自适应复原，而与退化纠缠的特征在解码过程中可能未经明确调控就被传播。现有方法大多忽略了这种双重作用，要么未充分利用退化线索，要么通过跳跃连接直接传递编码器特征。为解决这一问题，我们提出了PROTEUS模型，它将退化引导的特征适应与面向任务的潜在控制相结合。PROTEUS从两个互补视角解决该问题：在特征层面，引导动态特征调制模块利用空间变化的退化线索，在网络各阶段调整特征处理；在表示层面，面向任务的潜在控制器在判别性正则化下学习结构化控制码，并将其用于跳跃特征的通道级调制，无需该码形成度量上更清晰的嵌入。在5个配对和4个无参考水下基准上的大量实验表明，PROTEUS实现了极具竞争力的复原性能，在复原质量与计算成本间取得了良好平衡。
英文摘要
Degradation information in underwater images plays a dual role: its spatial and spectral cues can guide adaptive restoration, while degradation-entangled features may be propagated without explicit regulation during decoding. Existing methods largely overlook this dual role, either underexploiting degradation cues or directly forwarding encoder features through skip connections. To address this issue, we propose PROTEUS, which couples degradation-guided feature adaptation with task?oriented latent control. PROTEUS tackles this problem from two complementary perspectives. At the feature level, the Guided Dynamic Feature Modulation Block exploits spatially varying degradation cues to adapt feature processing across network stages. At the representation level, the task-oriented latent controller learns a structured control code under discriminative regularisation and uses it for channel-wise modulation of skip features, without requiring the code to form a metrically cleaner embedding. Extensive experiments on five paired and four non-reference underwater benchmarks demonstrate that PROTEUS achieves highly competitive restoration performance, with a favourable balance between restoration quality and computational cost.
